agent-evaluation

# Agent evaluation ## What to measure | Dimension | Examples | |-----------|----------| | Task success | End state matches spec (binary or rubric) | | Tool use | Correct tool, valid args, no spurious calls | | Safety | No policy violations, no secret leakage | | Efficiency | Tokens, latency, tool call count | | Stability | Same input -> consistent outcome across runs | ## Workflow 1. **Define tasks** — realistic user intents with clear pass/fail or scored rubric. 2. **Build dataset** — golden set + edge cases (errors, ambiguous input, empty context). 3. **Run baseline** — fixed model/settings; log traces (inputs, tools, outputs). 4. **Score** — automated checks first; human review for ambiguous cases. 5. **Compare** — A/B prompts, models, or tool schemas; report deltas with confidence notes. 6. **Gate** — block release on regression in must-pass tasks. ## Automated checks - Schema validation on tool arguments. - Assert final answer contains required fields or avoids forbidden content. - Snapshot tests for deterministic sub-steps where possible. ## Human rubric (when needed) Score 1-5 on: correctness, completeness, tone, safety. Document disagreements. ## Anti-patterns - Eval only on cherry-picked happy paths. - Changing task and model simultaneously without isolation. - No trace logs when debugging tool failures. ## Output Summary table: variant | success rate | avg tools | avg latency | notes.